The analysis of biomedical signals demonstrating complexity through recurrence plots is challenging. Quantification of recurrences is often biased by sojourn points that hide dynamic transitions. To overcome this problem, time series have previously been embedded at high dimensions. However, no one has quantified the elimination of sojourn points and rate of detection, nor the enhancement of transition detection has been investigated. This paper reports our on-going efforts to improve the detection of dynamic transitions from logistic maps and fetal hearts by reducing sojourn points. Three signal-based recurrence plots were developed, i.e. embedded with specific settings, derivative-based and m-time pattern. Determinism, cross-determinism and percentage of reduced sojourn points were computed to detect transitions. For logistic maps, an increase of 50% and 34.3% in sensitivity of detection over alternatives was achieved by m-time pattern and embedded recurrence plots with specific settings, respectively, and with a 100% specificity. For fetal heart rates, embedded recurrence plots with specific settings provided the best performance, followed by derivative-based recurrence plot, then unembedded recurrence plot using the determinism parameter. The relative errors between healthy and distressed fetuses were 153%, 95% and 91%. More than 50% of sojourn points were eliminated, allowing better detection of heart transitions triggered by gaseous exchange factors. This could be significant in improving the diagnosis of fetal state.

Abstract-Nearly 20 million premature and Low Birth Weight infants are born each year in developing countries, 4 million die within their first month. These deaths occur due to the unavailability or unreliability of traditional incubators. Moreover, although Telemedicine is helpful in rural areas, the shortage of healthcare providers have made it inaccessible in both basic healthcare. Thereby, traditional preterm baby and low-birth weight incubators and therapeutic techniques lack Telemedicine communication and feedback. The aim of our project is to develop an advanced portable and wireless-base incubator. We tend to provide an affordable, feasible, patient friendly and reliable premature baby incubator especially in low-income countries. The project focuses on the premature babies in the third trimester of pregnancy. The design is based on Wi-Fi and infrared technologies that measure the essential parameters that must be controlled for preemies. These parameters include the heart rate, oxygen level in the blood and temperature. Results showed the advanced design building blocks. The response of the generated power-voltage proves that the power can be regulated by the voltage. The thermal isolation can decrease the thermal lose and increase the time needed to drop temperature 6 times. In the room temperature of premature infant, 20 o C and 45 o C, the resistance decreases from 12.69 kΩ to 4.82 kΩ. The voltage and the temperature were inversely proportional. The heaters were efficient when tested on water. One of the major advantages of the device is that it surpasses existing techniques. As a future prospect more electronic components needs to be tested and features needs to be extracted.

International audience—Recurrence plots are nonlinear tools used to visual-ize the behavior of trajectories of Dynamic Systems. Occurrence of false points known as 'sojourn points' have biased recurrence plots. To solve this contentious issue, the use of high embedding dimension was proposed. However it required a lot of computa-tion and was based on the phase space. For that, we proposed in this paper to compare four quantification techniques, by dropping out sojourn points from the recurrence test of time series. Firstly, a recurrence plot and embedding of two were used as reference methods. Secondly, the number of points in the pattern used for testing recurrences was increased and a m-pattern recurrence plot was introduced. Thirdly, a single system's output and its corresponding derivative were proposed. Numerical inference showed that it was sufficient to work on a single measurement regardless of the degrees of freedom of the considered system and thus the embedding dimension. The proposed techniques succeeded in eliminating sojourn points. They provided a tool for a clean unbiased recurrence plots which permits better analysis of chaotic dynamic systems

This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.

International audienceThe purpose of this study is to make a comparison between the fluorescence emissions of fresh extracted human biopsies and fixed human biopsies, in order to evaluate the impact of fixation on autofluoresence signal. Our group is developing an endo-microscope to image brain tissues in-vivo, however to date, in order to validate our technology the easiest type of samples we can access are fixed samples. However, the fixation is still challenging. For that, we aim through this study to determine whether we should pursue to work on fixed samples or we should shift to work on fresh biopsies. Data were collected on spectroscopic, lifetime measurement and fluorescence imaging setups with visible and two-photon excitations wavelengths. Five fresh and five fixed samples are involved in the experiment. Endogenous fluorescence of fixed biopsies were calculated. Experimental results reveal that at 405 nm and 810 nm, the fresh samples have an intensity of fluorescence two times higher than that of fixed samples. However, for each fluorophore and each excitation wavelength, the lifetime for fresh samples is shorter than that for fixed samples. Still, further studies and investigations involving the comparison between different samples are required to strengthen our findings

This paper deals with the discrimination between suffering and healthy fetuses, by means of a delta-fuzzy-similarity entropy. This new descriptor of complexity is based on the derivative of the fuzzy-similarity entropy. It was tested on fetal heart rate time-series and compared to the approximated and similarity entropies. The main outcome was the possibility to improve 10% the specificity and the sensitivity as compared to approximate entropy. This very good performance confirms that the new descriptor can be a valuable alternative as compared to other standard descriptors.

This paper proposes a combined coarse-grained multifractal method to discriminate between distressed and normal foetuses. The coarse-graining operation was performed by means of a coarse-grained procedure and the multifractal operation was based on a structure function. The proposed method was evaluated by one hundred recordings including eighty normal foetuses and twenty distressed foetuses. We found that it was possible to discriminate between distressed and normal foetuses using the Hurst exponent, singularity, and Holder spectra.

Fetal heart rate discrimination is an evolving field in biomedical engineering with many efforts dedicated to avoid preterm deliveries by way of improving fetus monitoring methods and devices. Entropy analysis is a nonlinear signal analysis technique that has been progressively developed to improve the discriminability of a several physiological signals, with Kernel based entropy parameters (KBEPs) found advantageous over standard techniques. This study is the first to apply KBEPs to analyze fetal heart rates. Specifically, it explores the usability of the cutting-edge nonlinear KBEPs in discriminating between healthy fetuses and fetuses under distress. The database used in this study comprises 50 healthy and 50 distressed fetal heart rate signals with severe intrauterine growth restriction. The Cascade analysis investigates six kernel based entropy measures on fetal heart rates discrimination, and compares them to four standard entropies. The study presents a statistical evaluation of the discrimination power of each parameter (paired t-test statistics and distribution spread). Simulation results showed that the distribution ranges in 80% of the entropy parameters in the distressed heart group are higher than those in the healthy control group. Moreover, the results show that it is advantageous to choose Circular entropy then Cauchy entropy (p < 0.001) over the standard techniques, in order to discriminate fetal heart rates.